End-to-end Ascend NPU development: migrate CUDA/TensorFlow models to NPU, develop custom operators (AscendC, Triton, CATLASS), deploy vLLM/Verl inference services, profile and optimize performance, manage clusters and containers, and generate documentation and tests across the full AI training and inference pipeline.

Automate end-to-end ML performance investigations: research SOTA papers and architectures, generate phased plans, judge experimental methodologies, profile bottlenecks, run metric-improvement campaigns with atomic git commits, auto-rollback on regressions, and leverage specialist agents for data lifecycle and deep paper analysis.

Guardrail your AI/ML research workflow with an AI collaborator that searches literature using query variations, analyzes codebases and logs, designs minimal falsification experiments, records predictions, and audits bugs.

Delegate expert-level AI/ML workflows to specialized agents: engineer optimized prompts with evaluation and A/B testing, architect scalable LLM systems with RAG/LoRA fine-tuning, build production NLP pipelines for NER/classification/QA, and deploy optimized models via vLLM/Triton/Docker/K8s for reliability, performance, and cost control.